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In Journal of medical ultrasonics (2001)

Despite recent advances in imaging for myocardial deformation, left ventricular ejection fraction (LVEF) is still the most important index for systolic function in daily practice. Its role in multiple fields (e.g., valvular heart disease, myocardial infarction, cancer therapy-related cardiac dysfunction) has been a mainstay in guidelines. In addition, assessment of LVEF is vital to clinical decision-making in patients with heart failure. However, notable limitations to LVEF include poor inter-observer reproducibility dependent on observer skill, poor acoustic windows, and variations in measurement techniques. To solve these problems, methods for standardization of LVEF by sharing reference images among observers and artificial intelligence for accurate measurements have been developed. In this review, we focus on the standardization of LVEF using reference images and automated LVEF using artificial intelligence.

Kusunose Kenya, Zheng Robert, Yamada Hirotsugu, Sata Masataka


Artificial intelligence, Echocardiography, Ejection fraction, Machine learning